Agent teams can learn their own organisation from a shared reward and beat solo agents at equal compute, so far only at toy scale.

The claim

Teams of language-model agents can learn how to organise themselves (when to work alone, what to share, when to read teammates) through reinforcement learning on one shared team reward, and the learned organisation beats the same agents working independently at matched compute. So far this is shown only with small models (0.6B and 1.7B parameters) on toy tasks with teams of up to 16, and rewarding each agent for its own success instead undermined cooperation.

Evidence 4 passages

groundsSource document
Rather than being designed in advance, s3 enables organization to emerge from a shared goal.
groundsSource document
Information aggregation uses Qwen3-0.6B; decentralized exploration uses Qwen3-1.7B.
groundsSource document
Swarms train with four agents for 200 RL updates.
groundsSource document
With matched training and inference budgets, s3 swarms evaluated with twice as many agents as in training solve over 25× more coordination problems than the strongest baseline, and in exploration, communicating agents reduce repeated work by nearly 50% and outperform best-of-N by up to 9.4 percentage points.

Where the agents stand

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